Top Trends Shaping Pharma in 2024: AI, Gene Editing, Biosimilars and Real-World Data

The pharma sector is constantly evolving, and with several key patents expiring in the forthcoming years, companies must keep up to date with the latest trends to ensure the development of the next generation of treatments.

Analytics company Clarivate has released its annual Drugs To Watch report, outlining new frontiers and key upcoming developments in the pharmaceutical and biologics markets.

This year’s report features several key themes that may shape the coming year and beyond, including the use of artificial intelligence (AI), gene editing, biosimilars and real-world data.

AI and machine learning

With the release of ChatGPT, AI quickly became a hot topic with more people seeking ways to integrate it into both their work and daily lives. AI has a wide array of applications – including predicting the taste of beer, analyzing the “unhealthiness” of restaurant menus or assisting radiologists with breast cancer screening.

In the pharmaceutical industry, AI and machine learning (ML) have the potential to make waves across the entire drug development pipeline, from the initial formulation of lead compounds right the way through to optimizing the design of clinical trials, supporting pharmacovigilance and marketing insights.

Potential applications include:

• Identifying promising drug targets as well as designing new drug compounds.
• Assisting clinicians with the diagnosis of disease and interpreting medical imaging.
• Identifying existing drugs with the potential to be repurposed for other conditions, with reduced time and costs compare to novel drug discovery programs.
• Optimizing clinical trial processes such as patient recruitment and real-time monitoring of data.

AI’s impact is already evident, with the first drug candidates developed using AI/ML now entering Phase 2 clinical trials. One drug, INS018_055, is claimed to be the “first fully generative AI drug to reach human trials.” Over 500 trials relating to AI/ML were conducted in 2022, and these numbers are expected to grow year on year.

Pharmaceutical giant Moderna recently announced a partnership with OpenAI – famous for its chatbot, ChatGPT. Moderna seeks to apply AI technology across diverse operations – from legal departments all the way to R&D – using the partnership to improve productivity and safety alongside human-led reviews. An internal chatbot, known as mChat, has already been adopted by 80% of Moderna employees since its launch at the start of 2023. Additionally, another tool designed to enhance clinical trial development, DoseID, has been created to assist the clinical study team by helping to review and analyze clinical data and visualize large datasets. Overall, the AI partnership is seen as a key part of Moderna’s plan to launch multiple products over the next few years and translate into better patient outcomes.

“AI can help us to find patterns in data and, increasingly, to infer causality,” said Dr. Roger Palframan, head of US research at UCB, in an interview with Technology Networks.

“This can help us prioritize from a long list of potential drug target options and help us make better choices about the targets to go for.”

“At the same time, AI can help us to define the patient population we're looking for across many diseases,” Palframan explained.

Despite the growing promise, applying AI and ML to pharma is not without its pitfalls, and companies need to stay up to date in a rapidly evolving field. For example, regulations governing the use of AI technologies are beginning to be implemented and are ever-changing, requiring expertise to manage and keep abreast of the latest developments.

“This is such a fast-moving field, so the best thing that you can do is to be agile and adaptable,” Palframan said.

He continues: “I think having a vision for the future is great, but I'm not entirely sure how we get there. Therefore, as this evolves quickly, how do we set ourselves up for success to be agile and not constrain the power of AI, especially in early research? How do we free the AI to be able to impact discovery? How do we apply the appropriate risk and framework to some areas, such as patient-facing AI where it naturally needs to be more regulated? But also, how do we free it in other areas to be able to maximize its potential? The danger is applying blanket restrictions on everything that will limit innovation.”

Gene editing

Gene-editing technologies have exploded since the awarding of the Nobel Prize in Chemistry in 2020 to Emmanuelle Charpentier and Jennifer Doudna, the researchers behind the development of CRISPR-Cas9 gene-editing technology.

This has opened the door to various gene-editing therapies. For example, Casgevy^TM (exagamglogene autotemcel) – one of Clarivate’s Drugs To Watch in 2024 – is among the first of these therapies to be approved. It is used for both sickle-cell disease and β-thalassemia, using the CRISPR-Cas9 “genetic scissors” to prevent the expression of a gene that blocks the expression of a form of hemoglobin expressed in fetuses. After birth, this protein allows the production of adult hemoglobin. But when the adult form is faulty due to a mutation, Casgevy can be used to switch the body to use otherwise functional fetal hemoglobin instead. It effectively switches off the production of the faulty adult hemoglobin and switches fetal hemoglobin back on.

The UK’s approval of Casgevy was a world-first authorization of a CRISPR-based therapy. “This is a great step in the advancement of medical approaches to tackle genetic diseases we never thought would be possible to cure,” said Dr. Alena Pance, senior lecturer in genetics at the University of Hertfordshire, to the UK Science Media Centre.

“The exciting aspect of this is the strategy used for the gene editing, because blood diseases can be caused by a number of different mutations that it would be difficult to target individually.”

“The future of life-changing cures resides in CRISPR-based technology. Genome editing has the potential to transform medicine but is currently typically applicable to blood-based disorders due to the ability to transfuse blood,” said Dr. Helen O’Neill, program director of reproductive science and women’s health at University College London.

However, the genetic diversity and complexity underlying the pathophysiology of disease remain a significant hurdle for the design of gene therapies.

During a panel discussion at Technology Networks' Cell & Gene Therapy online symposium, Dr. Marti Bernardo-Faura, a senior research scientist at Clarivate, explained: “Diseases often have complex genetic underpinnings with variants across populations.”

He continues: “You may have the same phenotype for a given disease, but different mutations that need to be edited for different patients, so designing therapies that are effective across this genetic diversity is a formidable challenge.”

Biosimilars

Biosimilars are officially approved biopharmaceutical drugs designed to have properties similar to an already-licensed drug, referred to as the reference product. In other words, they are a near-identical copy of a biopharmaceutical originally produced by another company when the original patent expires.

Biosimilars have no clinically meaningful differences from the original product and can increase access to innovative therapeutics – potentially at a lower cost, thereby increasing patient access.

“[Biosimilars] are generally less expensive than the originator drug, and once biosimilars enter the market, originator prices tend to fall in response to the lower-cost competition,” explained Dr. Karen Van Nuys, a senior fellow at the University of South California Schaeffer Center for Health Policy and Economics.

The first biosimilar was approved by the US Food and Drug Administration in 2015, but the adoption of biosimilars has been a mixed bag. This includes Humira® (adalimumab), a monoclonal antibody used to treat certain types of arthritis and inflammation-related diseases such as rheumatoid arthritis and Crohn’s disease.

“A recent example is biosimilars for Humira – several have entered the market, all with lower prices compared to the originator, but the biosimilars are not getting onto formularies because they don’t pay the biggest rebates,” said Van Nuys. “So, patients cannot access the low-cost biosimilars – either they’re excluded from the formulary, or they require higher out-of-pocket payments relative to the branded product (even though they cost less in total).”

Humira retained a large share of the market alongside the launch of eight biosimilars after its patent expiring in January 2023 despite sales suffering a 31% drop in the following 9-month period.

“This is dramatically impacting the uptake of Humira biosimilars, and as a result, they are not reducing overall costs in that market as rapidly as they could,” Van Nuys added. “I think this dynamic will eventually draw greater scrutiny from self-insured employers and policymakers.”

On the other hand, another originator drug called Herceptin (trastuzumab) – used for some breast cancers – showed continued decreases in market share with the launch of five subsequent biosimilars. The originator lost approximately half of the market and the prices of all six options declined, according to a study led by Van Nuys.

“Trastuzumab is a good example of how US markets for physician-administered biologic drugs behave after biosimilar entry,” she explained. “This molecule attracted five biosimilar competitors in just one year, and they all entered the market at a lower price than the originator.”

“After 3 years, the average sales prices of the biosimilar products ranged from 28% to 58% of the originator’s average selling price (ASP) before the competition, and the originator’s ASP had fallen by 21%, as the lower-cost biosimilars steadily gained market share.”

“Initially, US providers seemed somewhat hesitant to prescribe biosimilars in place of originator biologics, especially when it meant switching individual patients from one version to the other,” Van Nuys said. “With the earliest US biosimilars, doctors were slower to prescribe the biosimilar when it was treating a chronic condition, which would mean shifting patients who were already taking the originator version onto the new drug.”

“By comparison, doctors switched to the biosimilar more quickly when it was treating an acute condition where patients were altogether new to treatment, and didn’t have to switch from a different regimen.”

However, these challenges may gradually shrink over time as physicians become more familiar with biosimilars, with economic hurdles remaining, Van Nuys explains: “The main challenges in the US now appear to be economic, particularly in getting biosimilars placed on formularies with favorable terms relative to the originator.”

Real-world data

Real-world data (RWD) provides information on drugs when used outside of clinical research settings. For example, sources of RWD include electronic health records, disease registries, wearable devices and insurance records – meaning this data differs significantly from randomized controlled trials.

RWD can also help to inform study design for clinical trials and is typically cheaper and faster to acquire than clinical trial data. Analysis of RWD can shorten drug development timelines and help with post-market surveillance, providing insights into how patients continue to respond to the drug after approval.

“Regulatory agencies are increasingly accepting real-world evidence in support of therapy approvals and post-market surveillance,” said Bernardo-Faura.

“That helps to address regulatory challenges by providing evidence of how therapies perform in the real world.”

RWD also reflects the long-term benefits of therapies, particularly important for chronic conditions. It can also help to provide more data on patient groups that can be under-represented in randomized controlled trials to determine which drugs work best in these groups. As rigorous trials take place in well-defined patient populations in extremely regulated conditions, RWD can help us see how drugs measure up in “real” conditions.

Furthermore, AI and machine learning can fit into the RWD landscape by enabling the more granular analysis of vast datasets. However, challenges surrounding its use include the availability and access to high-quality, reliable and relevant RWD, as well as concerns surrounding patient privacy and data.

“When you look at real-world data, we have much more breadth and depth of data in the United States, so I think that’s probably the first market that any company should consider,” explained Khurram Nawaz, senior manager of Healthcare Research and Data Analytics (Oncology) at Clarivate. “Real-world data can help to better understand the real-world uptake of these drugs.”

Shaping the future of pharma

These innovations are reshaping the industry and driving progress, helping to secure the development of future treatments. Strategies to streamline drug development with AI, widen accessibility with biosimilars and utilize more representative real-world data are examples of key trends to watch out for in 2024, showcasing the constantly evolving pharma landscape.

About the interviewees:

Dr. Roger Palframan is the head of US Research at UCB, where he has led the development of the company’s gene therapy research platform and is responsible for developing UCB’s US research capabilities. He holds a PhD in immunology from Imperial College London and was a Wellcome Trust Postdoctoral Fellow at Harvard Medical School.

Dr. Marti Barnardo-Faura is a senior research scientist at Clarivate and holds a PhD from the University of Heidelberg.

Dr. Karen Van Nuys is a senior fellow at the USC Schaeffer Center for Health Policy and Economics and the executive director of the Value of Life Sciences Innovation program. She holds a PhD in economics from Stanford University, and her research focuses on the pharmaceutical distribution system and the impact of intermediaries’ business practices on prescription drug utilization and cost.

Khurram Nawaz is the senior director of Oncology Market Assessment at Clarivate.